Parkinson’s disease is associated with motor symptoms, including tremor. The DBS (Deep Brain Stimulation) involves electrode implantation into sub-cortical structures for long-term stimulation at frequencies greater than 100Hz. We performed linear and nonlinear analysis of the tremor signals to determine a set of parameters and rules for recognizing the behavior of the investigated patient and to characterize the typical responses for several forms of DBS. We found patterns for homogeneous group for data reduction. We used Data Mining and Knowledge discovery techniques to reduce the number of data. To support such predictions, we develop a model of the tremor, to perform tests determining the DBS reducing the tremor or inducing tolerance and lesion if the stimulation is chronic.